Latest / AI Fire Daily / #614 Neil: AI Coding Agent Battle Which One Builds Better Websites?

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#614 Neil: AI Coding Agent Battle Which One Builds Better Websites? · AI Fire Daily

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  It's fascinating to watch the software world
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  right now. We really have to wonder, are we still
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  building software? Or are we just managing the
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  AI builders? It's a profound shift. Right, and
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  it's shifting incredibly fast. Welcome to the
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  deep dive. Today, we're taking you straight into
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  the middle of a massive debate. You see it everywhere
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  online. You really do. We're settling the endless
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  argument between two major AI coding agents.
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  Claude Code and Codex. people get very attached
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  to their tool. Oh, they totally do. Like you
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  read one developer forum and Cloud users swear
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  its design logic is unmatched. Then you check
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  another thread and Codex users say it's faster,
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  cleaner, just superior in every single way. It's
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  honestly exhausting. It is. But today we're setting
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  aside the Internet opinions. We're looking at
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  hard data. Exactly. We are walking you through
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  three rigorous head -to -head tests. We use the
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  exact same source material for both AIs. But
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  we fundamentally change the prompt style. for
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  each test. We used a controlled prompt, an open
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  -ended prompt, and a hyper -detailed prompt.
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  Beat. We want to see how these models behave
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  when we change their constraints. Because that
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  is how you find the truth. Testing an AI in just
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  one environment doesn't tell you the whole story.
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  We're going to see which AI actually performs
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  better under vastly different real -world conditions.
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  Let's start by looking at raw efficiency first.
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  Test number one. The controlled baseline test.
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  This is where we keep things incredibly simple
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  and completely fair. We want to see their raw
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  speed here. We want to observe their baseline
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  decision -making logic. The project was a website
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  for a service called Bowlin Bloom. I really love
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  this concept, by the way. Yeah. Yeah, it's a
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  meal care box service, but it's specifically
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  designed for the first 14 days after bringing
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  a new baby home. It's such a specific, deeply
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  helpful product. It's a very strong concept,
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  and both AI coding agents received the exact
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  same input. They got the business idea, they
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  got the website copy, they got the brand guidelines
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  and the logo, and the technical requirements,
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  plus the specific meal options, which was key.
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  Yes. Four meals in total. Chicken, red lentil,
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  garden greens, and miso mushroom. Sounds good.
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  The prompt asked them to act as a senior product
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  designer. They needed to build a production quality
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  website. make the meals easy to scan, and build
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  a clear, functional checkout flow. But the key
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  here is they were told explicitly not to invent
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  a new visual identity. Right. They had rules.
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  Exactly. They had to use the provided assets.
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  The prompt gave them a goal, but it didn't micromanage
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  them. It didn't tell them where to put every
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  single button. Looking at the data for Claude
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  Code, I'm struck by how heavily it leaned on
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  traditional UI patterns. How exactly did it execute
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  the build? Well, Claude Code did a really solid,
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  safe job. It followed the baseline instructions
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  very well. It broke the how it works section
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  into four very clear, distinct steps. And the
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  meal section had all the right details. Prep
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  times, dietary notes, links to the ingredients.
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  And the interactive elements, like the box builder
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  itself. It worked perfectly. You could choose
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  a box size, change meal quantities, select delivery
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  dates. It was very complete, but there was a
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  massive catch. The computational resources required
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  to get there. A huge resource drain. Claude Code
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  took about two hours to finish this one build.
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  Two hours. Two full hours. It used four separate
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  subagents to get it done. Let's unpack that term
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  quickly. Subagents are smaller specialized AI
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  programs that handle specific subtasks. Yeah,
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  exactly. One might handle HTML, another handle
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  styling, another checks the logic. But all that
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  back and forth communication takes a toll. Right.
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  It generated roughly 500 ,000 output tokens,
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  and the estimated API cost for the single test
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  was $53. Output tokens. For those wondering,
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  that is the exact number of word pieces the AI
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  generates. Exactly. Half a million tokens for
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  a simple landing page is a massive amount of
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  data to process. It's incredibly heavy. It's
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  a total brute force approach to problem solving.
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  It feels like a model constantly second guessing
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  itself. It's running iterative loops, rewriting
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  its own code over and over. Now let's look at
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  Codex. Codex was a completely different story.
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  It was so much faster. How fast? It finished
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  the entire build in just 50 minutes. Wow. Yeah,
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  and it used a single sub -agent for the whole
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  task. It generated only 100 ,000 tokens, an API
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  cost under $20. Whoa, imagine scaling that to
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  a billion queries. The cost difference would
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  be absolutely astronomical. You'd be bankrupting
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  your engineering department running Claude at
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  that scale. You really would. Maybe. Managing
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  this data, should feel smooth. It should feel
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  like stacking Lego blocks of data. Yeah. Codex
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  just did it with far fewer blocks. Right. And
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  the crazy part is, Codex actually looked better.
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  Really? Yeah. It didn't just write linear code.
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  It added these incredibly smart little UI touches.
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  Like, it put a small detail card right on top
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  of the product image. It just noted that the
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  meals are freezer -ready and made for two adults.
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  It synthesized the poppy on its own. Exactly.
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  That clarifies the offer instantly. It shows
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  a semantic understanding of postpartum needs,
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  not just regurgitating strings of text. It really
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  does. It makes the page so much better. The meal
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  section was stronger, too. How so? Codex gave
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  each meal more useful information without crowding
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  the layout. It balanced speed, clarity, and visual
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  polish perfectly. It seems like a clear win for
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  Codex. But let me play devil's advocate here.
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  OK. Beat. If Claude's four sub -agents are constantly
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  double -checking their work, maybe they catch
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  a subtle security flaw in a checkout flow that
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  a single -pass model like Codex simply misses.
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  Are we overvaluing raw speed? I mean, why does
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  token efficiency matter so much for a solo developer?
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  Because of the compounding costs. If you're just
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  building one tiny script as a weekend hobby,
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  a $50 API bill is totally fine. Right. But if
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  you rely on an AI coding agent as your daily
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  creative partner, you're running constant iterations.
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  You're fixing bugs. You're refactoring code all
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  day long. Yeah. That $50 turns into $500, then
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  $5 ,000 very quickly. So high token yes -age
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  quietly drains your budget. and slows everything
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  down. Nailed it. It dictates what you can actually
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  afford to build. When you're waiting two hours
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  for a single iteration, your creative momentum
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  completely stalls out. That makes sense. Which
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  is why Codex took an easy win on this baseline
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  test. It makes complete sense. We've seen how
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  they handle a controlled baseline beat. But enterprise
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  environments are rarely that simple. For the
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  second test, we wanted to completely shift the
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  paradigm. We wanted to measure their actual creative
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  taste. The open -ended design test. This is where
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  we strip away all the guardrails. We wanted to
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  see what happens when the AI has to act as the
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  creative director. No brand guidelines were provided
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  this time. No logos, no layout hints whatsoever.
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  Nothing. The AIs had to invent a consumer brand
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  completely from scratch. Two secs silence. I
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  still wrestle with prompt drift myself. When
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  I give an AI too much freedom, it often loses
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  the core vision entirely. Oh, absolutely. It's
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  a very common struggle. The AI desperately wants
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  to be helpful, so it just starts hallucinating
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  features. It changes color palettes halfway down
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  the page. It completely forgets its own foundational
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  rules. Yeah. But in this test, they had to maintain
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  a cohesive brand from top to bottom. This speaks
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  to the underlying training of the models. What
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  kind of brand did Claude Code build for this
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  open -ended test? Claude invented a brand called
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  FieldNote. It's designed as a short skill learning
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  platform. That's a highly practical concept.
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  Yeah. The pitch is that you learn one practical
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  skill a week. Just 15 to 25 minute lessons. No
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  massive intimidating courses you'll never actually
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  finish. And how was the design execution? How
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  did it handle the aesthetic choices? It was very
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  editorial. It was very, very safe. Safe. Yeah.
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  It used lots of traditional serif fonts. The
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  primary color palette was cream and dark green.
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  It had this specimen card UI that looked perfectly
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  nice, but a bit generic. This makes sense when
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  you understand RLHF. Reinforcement learning from
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  human feedback. Models like Claude are heavily
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  aligned for safety, neutrality, and helpfulness.
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  That often translates directly into aesthetic
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  neutrality. It leans on safe. corporate UI patterns.
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  Exactly. It felt very traditional. The categories
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  were practical, things like at the desk and at
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  the home. Right. It even included a pricing toggle
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  and a syllabus accordion. It was incredibly consistent
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  mechanically. It was a new drift. There was no
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  weird prompt drift at all, but it relied heavily
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  on basic text and standard cards. It lacked any
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  real visual punch. It played it entirely safe.
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  The weights of its training clearly favor standard
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  sauce dashboard templates. What about Codex?
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  Codex went a totally different route. It felt
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  way more adventurous. It invented a brand called
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  Room Weather. And Room Weather, that implies
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  an environmental shift. Yes, it's a fictional
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  home fragrance brand. The core product is a ceramic
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  diffuser with swappable scent fronts. Interesting.
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  It essentially lets you change the mood of an
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  entire room. That sounds like a legitimate modern
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  direct -to -consumer startup. It really does.
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  And the interface design was absolutely brilliant.
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  Codex didn't just build spatic cards, it built
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  this interactive feature called a scent forecast.
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  Let's dig into the mechanics of that. How did
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  that interaction actually work? You could click
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  these different scent profiles directly on the
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  page. It gave you options like clear desk, after
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  rain, green hour, or low tide. Wow. And when
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  you click them, the descriptions and the supporting
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  visual details actually change dynamically on
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  the screen. The interaction itself actually helps
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  you understand the physical product. That requires
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  a sophisticated understanding of state management.
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  It maps specific user clicks to dynamic content
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  rendering. It's a very human design choice. Right.
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  It felt totally launch ready. It wasn't just
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  a basic coding experiment. It felt like a real
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  living consumer brain. Yeah. Codex took the win
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  again here for originality, aesthetic taste,
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  and the sheer quality of the interaction. Looking
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  at these vastly different results, let me ask
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  you this. Are we really evaluating coding ability
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  here? Or are we just grading an AI's default
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  design taste? That is the crucial question. We
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  are absolutely grading their default taste here.
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  The actual underlying React code required for
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  Claude's boring card layout and Codex's beautiful
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  interactive layout is often very similar in complexity.
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  The syntax isn't the differentiator. The difference
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  is the creative choices the AI makes when you
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  leave it alone. Codex simply has better out -of
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  -the -box taste. Right. We're really grading
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  the AI era's default design taste, not just its
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  code. Exactly. It's about design intuition. Codex's
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  latent space seems to heavily favor modern, high
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  -conversion Shopify templates when prompted with
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  consumer products. And Claude. Claude leans toward
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  dry corporate wikis. We will continue exploring
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  how constraints change this dynamic right after
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  this short break. provided separately. Welcome
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  back to the deep dive. So far, Codex has won
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  the rigid baseline test with raw efficiency.
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  Yeah. It has also won the open -ended creative
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  test with superior design taste. It seems to
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  have a massive clear advantage over Claude code.
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  It really does. It feels like a total shutout
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  right now. But test three is where the data gets
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  really fascinating. It is. If Codex thrives in
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  a creative vacuum, we need to test the opposite
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  extreme, beat. What happens to that creative
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  advantage when we lock down every single pixel?
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  We are talking about a hyper detailed specification
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  test. This is the kind of strict unforgiving
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  brief that makes human developers sweat. The
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  project was a fictional sauce brand. It was called
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  Northstar. Right. It's a workspace for independent
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  freelancers to manage projects, invoices, and
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  cash flow. But the prompt was incredibly rigid
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  this time. We dictated the exact layout mechanics.
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  We demanded a 4654 split for the hero section.
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  We gave it exact hex colors to use. The background
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  had to be F4F1EA. The primary green was exactly
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  315E46. Yes. We specified the exact typography
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  stack, DM sans for the body text, instrument
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  serif for the headlines. We even gave it the
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  exact mock data for a complex dashboard interface.
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  It had to build a realistic dashboard using only
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  raw code. No stock images were allowed. Exactly.
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  It had to show exactly $8 ,420 in monthly revenue.
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  And exactly $3 ,200 in outstanding invoices.
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  Wow. It had to render three active projects,
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  a six -month revenue chart, dynamic invoice status
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  pills. This requires deep component architecture.
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  We also require three clickable tabs, projects,
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  invoices, and cash flow. Right. Clicking those
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  tabs had to instantly update the dashboard content
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  without reloading the page. A very strict set
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  of state management rules. We even mandated a
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  horizontal four -step workflow timeline on the
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  desktop view. What were the steps? Add a client,
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  start a project, send the invoice, track the
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  payment. We specified a testimonial from Maya
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  Chen, independent brand designer. We specified
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  two distinct pricing tiers. Solo for $0, Studio
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  for $18. It was so relentlessly strict, it leaves
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  absolutely no room for creative interpretation.
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  So how do you think Cloud Code handled this?
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  Well, given its earlier performance, you'd expect
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  it to struggle with the complex interactivity.
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  And it didn't. It executed it perfectly. Flawlessly,
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  even. It kept the exact two -column layout. Yeah,
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  it used the exact hex colors provided. It built
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  the complex dashboard with the precise revenue
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  numbers. It alternated the text and visual positions
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  for the feature sections exactly as requested.
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  It didn't try to improve the prompt. It didn't
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  try to add hallucinated features. Not at all.
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  It just blindly followed orders. The cream background,
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  the dark green palette, the specific typography.
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  It all stayed perfectly true to the spec. This
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  showcases how attention mechanisms work in large
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  language models. When you provide strict constraints,
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  you're actually narrowing the model's attention
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  heads. beat. It stops searching the latent space
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  for novel ideas and simply executes the localized
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  instructions. Exactly. So how did Codex perform
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  under these exact same constraints? Codex did
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  the exact same thing. A dead tie. A complete
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  and total tie. Both models absolutely nailed
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  the specification. The layouts were remarkably
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  similar. The complex tabs worked perfectly without
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  reloading. The exact hex colors matched perfectly.
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  When you remove the creative freedom entirely,
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  the massive gap between Claude code and Codex
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  completely disappears. That is deeply interesting.
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  What does this dead tie actually reveal about
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  the whole Claude versus Codex debate when we
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  talk about building real enterprise software?
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  It reveals that the entire debate might be focusing
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  on the wrong variables entirely. How so? Well,
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  when you're building enterprise software, you
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  usually have a very strict design system already
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  in place. You have highly specific data requirements.
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  You aren't asking the AI to be a wild creative
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  director. You are asking it to execute a highly
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  detailed, rigid blueprint. And what this third
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  test definitively proves is that both models
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  are incredibly good at executing strict blueprints.
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  Basically, strict instructions level the playing
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  field between different AI models. That is exactly
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  the ultimate takeaway here. It really forces
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  you to rethink how we approach these tools. Let's
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  synthesize this whole deep dive for you. Let's
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  look at the bigger picture. Let's do it. If you're
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  starting with just a rough idea, if you need
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  an AI to act as a creative partner, to handle
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  the design thinking, to invent user interactions
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  from scratch, Codex is the clear undisputed winner.
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  Hands down, it's exponentially faster. It's significantly
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  cheaper in terms of overall token usage. It has
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  much better out of the box visual polish. It
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  handles open -ended ambiguity just like a real
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  senior human designer would. If you have a highly
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  detailed specification, if you know exactly what
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  you want down to the exact hex code, the typography
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  stack, and the precise interaction state, The
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  gap between them totally vanishes. It really
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  does. They both become excellent, obedient executors
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  of your vision. The ultimate lesson here is about
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  control and instruction. When your prompt is
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  meticulously detailed, the quality of your prompt
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  matters far more than which AI coding agent you
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  happen to choose. That is the honest truth of
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  it. We need to stop fighting over which AI brand
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  is inherently better on a forum and start getting
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  better at telling them exactly what we need them
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  to do. The prompt itself is the product now.
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  It leads to an incredibly interesting thought,
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  Bea. If hyper -detailed specs make the choice
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  of AI completely irrelevant, you really have
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  to wonder. Will the entire future of software
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  engineering just be the mastery of writing the
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  perfect specification? Will the whole which AI
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  is better debate eventually become entirely obsolete?
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  That is a massive, highly philosophical question
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  to leave everyone with. I absolutely love it.
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  And hey, do not just take our word for it. Right.
• 17:14
  Thank you so much for joining us on this deep
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  dive today. I highly encourage you to go test
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  your own prompts, run your hardest, most complex
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  specs on both models, see if your own results
• 17:23
  match the data we unpack today. You might be
• 17:25
  very surprised by what you find. Until next time,
• 17:28
  keep building.